我更喜欢这样拉:

>>> lst = [(1, u'abc'), (2, u'def')]
>>> new, _ = zip(*lst)
>>> new
(1, 2)
>>> 

或者如果你不知道有多少额外的值

>>> new, *_ = zip(*lst)
>>> new
(1, 2)
>>> 

要获取列表或元组的元素，可以遍历列表或元组

a = [(1, u'abc'), (2, u'def')]

list1 = [a[i][0] for i in range(len(a))]

print(list1)

这些是元组，不是集合。你可以这样做:

l1 = [(1, u'abc'), (2, u'def')]
l2 = [(tup[0],) for tup in l1]
l2
>>> [(1,), (2,)]

你可以解包你的元组，使用列表推导式只得到第一个元素:

l = [(1, u'abc'), (2, u'def')]
[f for f, *_ in l]

输出:

[1, 2]

不管你在元组中有多少个元素，这都是有效的:

l = [(1, u'abc'), (2, u'def', 2, 4, 5, 6, 7)]
[f for f, *_ in l]

输出:

[1, 2]

我更喜欢这样拉:

>>> lst = [(1, u'abc'), (2, u'def')]
>>> new, _ = zip(*lst)
>>> new
(1, 2)
>>> 

或者如果你不知道有多少额外的值

>>> new, *_ = zip(*lst)
>>> new
(1, 2)
>>> 

我认为比较不同方法的运行时可能是有用的，所以我做了一个基准测试(使用simple_benchmark库)

I)具有2个元素的元组的基准测试

由于您可能希望通过索引0从元组中选择第一个元素，因此通过期望恰好有2个值，0显示出非常接近解包解决方案的最快解决方案

import operator
import random

from simple_benchmark import BenchmarkBuilder

b = BenchmarkBuilder()



@b.add_function()
def rakesh_by_index(l):
    return [i[0] for i in l]


@b.add_function()
def wayneSan_zip(l):
    return list(list(zip(*l))[0])


@b.add_function()
def bcattle_itemgetter(l):
     return list(map(operator.itemgetter(0), l))


@b.add_function()
def ssoler_upacking(l):
    return [idx for idx, val in l]

@b.add_function()
def kederrack_unpacking(l):
    return [f for f, *_ in l]



@b.add_arguments('Number of tuples')
def argument_provider():
    for exp in range(2, 21):
        size = 2**exp
        yield size, [(random.choice(range(100)), random.choice(range(100))) for _ in range(size)]


r = b.run()
r.plot()

II)基准具有2个或更多元素的元组

import operator
import random

from simple_benchmark import BenchmarkBuilder

b = BenchmarkBuilder()

@b.add_function()
def kederrack_unpacking(l):
    return [f for f, *_ in l]


@b.add_function()
def rakesh_by_index(l):
    return [i[0] for i in l]


@b.add_function()
def wayneSan_zip(l):
    return list(list(zip(*l))[0])


@b.add_function()
def bcattle_itemgetter(l):
     return list(map(operator.itemgetter(0), l))


@b.add_arguments('Number of tuples')
def argument_provider():
    for exp in range(2, 21):
        size = 2**exp
        yield size, [tuple(random.choice(range(100)) for _
                     in range(random.choice(range(2, 100)))) for _ in range(size)]

from pylab import rcParams
rcParams['figure.figsize'] = 12, 7

r = b.run()
r.plot()